Authentication systems usually adopt either the conventional identifier-password paradigm or different kinds of tokens (e.g., badges, keys). However, passwords can be disclosed while being input and tokens can be stolen and used by impostors. As a result, in the last decades biometric techniques were developed to identify a user through physiological features that cannot be stolen or counterfeited. However, even those techniques have their flaws, and for this reason recent research addressed the combination of multiple identification factors. In this context, this work proposes VisilabFaceRec, a multi factor authentication system based on the combination of a dual-stage cascading classifier for biometric identification (face recognition) with an encrypted RFID tag for token-based authentication. Unlike other approaches in the literature that propose a centralized database for storing biometric data, with
serious risks regarding user privacy, our work avoids a centralized database and stores sensitive data in the RFID, thus also making the
system performance independent of the total number of subjects enrolled. The proposed architecture is able to simultaneously minimize the False Acceptance Rate and the False Rejection Rate, thanks to an innovative approach for the calculation of the decision thresholds for the two discriminators. VisilabFaceRec has been realized on a commercial board for embedded computing and proven to be able to run in near real-time. The paper describes the system architecture and the algorithm used to jointly determine the couple of decision thresholds for the cascading classifiers, and proposes a performance evaluation, in terms of both accuracy and speed, on a well-known and publicly available face database.